# HG changeset patch
# User Matthias Mauch <mail@matthiasmauch.net>
# Date 1289438878 -32400
# Node ID ba930176df5b8884baed3be77863f1bd3438cdcf
# Parent  d398e73b46e0ee590c8572027e3d43833ff1e504
fixed. warning: I added some arrays with variable length (though not at runtime).

diff -r d398e73b46e0 -r ba930176df5b Chordino.cpp
--- a/Chordino.cpp	Wed Nov 10 22:52:46 2010 +0900
+++ b/Chordino.cpp	Thu Nov 11 10:27:58 2010 +0900
@@ -298,11 +298,11 @@
         currentTunedSpec.values.push_back(0.0); currentTunedSpec.values.push_back(0.0); currentTunedSpec.values.push_back(0.0); // upper edge
         vector<float> runningmean = SpecialConvolution(currentTunedSpec.values,hw);
         vector<float> runningstd;
-        for (int i = 0; i < 256; i++) { // first step: squared values into vector (variance)
+        for (int i = 0; i < nNote; i++) { // first step: squared values into vector (variance)
             runningstd.push_back((currentTunedSpec.values[i] - runningmean[i]) * (currentTunedSpec.values[i] - runningmean[i]));
         }
         runningstd = SpecialConvolution(runningstd,hw); // second step convolve
-        for (int i = 0; i < 256; i++) { 
+        for (int i = 0; i < nNote; i++) { 
             runningstd[i] = sqrt(runningstd[i]); // square root to finally have running std
             if (runningstd[i] > 0) {
                 // currentTunedSpec.values[i] = (currentTunedSpec.values[i] / runningmean[i]) > thresh ? 
@@ -348,12 +348,12 @@
         currentChromas.hasTimestamp = true;
         currentChromas.timestamp = currentTunedSpec.timestamp;    
 
-        float b[256];
+        float b[nNote];
 	
         bool some_b_greater_zero = false;
         float sumb = 0;
-        for (int i = 0; i < 256; i++) {
-            // b[i] = m_dict[(256 * count + i) % (256 * 84)];
+        for (int i = 0; i < nNote; i++) {
+            // b[i] = m_dict[(nNote * count + i) % (nNote * 84)];
             b[i] = currentTunedSpec.values[i];
             sumb += b[i];
             if (b[i] > 0) {
@@ -399,11 +399,11 @@
                 float zz[84+1000];
                 int indx[84+1000];
                 int mode;
-                int dictsize = 256*signifIndex.size();
+                int dictsize = nNote*signifIndex.size();
                 float *curr_dict = new float[dictsize];
                 for (unsigned iNote = 0; iNote < signifIndex.size(); ++iNote) {
-                    for (unsigned iBin = 0; iBin < 256; iBin++) {
-                        curr_dict[iNote * 256 + iBin] = 1.0 * m_dict[signifIndex[iNote] * 256 + iBin];
+                    for (unsigned iBin = 0; iBin < nNote; iBin++) {
+                        curr_dict[iNote * nNote + iBin] = 1.0 * m_dict[signifIndex[iNote] * nNote + iBin];
                     }
                 }
                 nnls(curr_dict, nNote, nNote, signifIndex.size(), b, x, &rnorm, w, zz, indx, &mode);
diff -r d398e73b46e0 -r ba930176df5b NNLSBase.cpp
--- a/NNLSBase.cpp	Wed Nov 10 22:52:46 2010 +0900
+++ b/NNLSBase.cpp	Thu Nov 11 10:27:58 2010 +0900
@@ -358,14 +358,14 @@
         hw.push_back(0.54 - 0.46 * cos((2*M_PI*i)/(hamwinlength-1)));    
         hamwinsum += 0.54 - 0.46 * cos((2*M_PI*i)/(hamwinlength-1));
     }
-    for (int i = 0; i < hamwinlength; ++i) hw[i] = round(hw[i] / hamwinsum * 10000)*1.0/10000;
+    for (int i = 0; i < hamwinlength; ++i) hw[i] = hw[i] / hamwinsum;
 	
     if (channels < getMinChannelCount() ||
 	channels > getMaxChannelCount()) return false;
     m_blockSize = blockSize;
     m_stepSize = stepSize;
     m_frameCount = 0;
-    int tempn = 256 * m_blockSize/2;
+    int tempn = nNote * m_blockSize/2;
     // cerr << "length of tempkernel : " <<  tempn << endl;
     float *tempkernel;
 
@@ -577,11 +577,11 @@
         f2.values.push_back(0.0); f2.values.push_back(0.0); f2.values.push_back(0.0); // upper edge
         vector<float> runningmean = SpecialConvolution(f2.values,hw);
         vector<float> runningstd;
-        for (int i = 0; i < 256; i++) { // first step: squared values into vector (variance)
+        for (int i = 0; i < nNote; i++) { // first step: squared values into vector (variance)
             runningstd.push_back((f2.values[i] - runningmean[i]) * (f2.values[i] - runningmean[i]));
         }
         runningstd = SpecialConvolution(runningstd,hw); // second step convolve
-        for (int i = 0; i < 256; i++) { 
+        for (int i = 0; i < nNote; i++) { 
             runningstd[i] = sqrt(runningstd[i]); // square root to finally have running std
             if (runningstd[i] > 0) {
                 // f2.values[i] = (f2.values[i] / runningmean[i]) > thresh ? 
@@ -637,12 +637,12 @@
         f6.hasTimestamp = true;
         f6.timestamp = f2.timestamp;
 	        
-			float b[256];
+			float b[nNote];
 	
         bool some_b_greater_zero = false;
         float sumb = 0;
-        for (int i = 0; i < 256; i++) {
-            // b[i] = m_dict[(256 * count + i) % (256 * 84)];
+        for (int i = 0; i < nNote; i++) {
+            // b[i] = m_dict[(nNote * count + i) % (nNote * 84)];
             b[i] = f2.values[i];
             sumb += b[i];
             if (b[i] > 0) {
@@ -690,12 +690,12 @@
 				    float zz[84+1000];
                 int indx[84+1000];
                 int mode;
-                int dictsize = 256*signifIndex.size();
+                int dictsize = nNote*signifIndex.size();
                 // cerr << "dictsize is " << dictsize << "and values size" << f3.values.size()<< endl;
 					float *curr_dict = new float[dictsize];
                 for (unsigned iNote = 0; iNote < signifIndex.size(); ++iNote) {
-                    for (unsigned iBin = 0; iBin < 256; iBin++) {
-                        curr_dict[iNote * 256 + iBin] = 1.0 * m_dict[signifIndex[iNote] * 256 + iBin];
+                    for (unsigned iBin = 0; iBin < nNote; iBin++) {
+                        curr_dict[iNote * nNote + iBin] = 1.0 * m_dict[signifIndex[iNote] * nNote + iBin];
                     }
                 }
 					nnls(curr_dict, nNote, nNote, signifIndex.size(), b, x, &rnorm, w, zz, indx, &mode);
diff -r d398e73b46e0 -r ba930176df5b NNLSChroma.cpp
--- a/NNLSChroma.cpp	Wed Nov 10 22:52:46 2010 +0900
+++ b/NNLSChroma.cpp	Thu Nov 11 10:27:58 2010 +0900
@@ -99,7 +99,7 @@
     d2.description = "A Log-Frequency Spectrum (constant Q) that is obtained by cosine filter mapping, then its tuned using the estimated tuning frequency.";
     d2.unit = "";
     d2.hasFixedBinCount = true;
-    d2.binCount = 256;
+    d2.binCount = nNote;
     d2.hasKnownExtents = false;
     d2.isQuantized = false;
     d2.sampleType = OutputDescriptor::FixedSampleRate;
@@ -243,6 +243,9 @@
     float thresh = pow(10,dbThreshold/20);
     // cerr << "tune local ? " << m_tuneLocal << endl;
     int count = 0;
+
+    cerr << nNote;
+    cerr << endl << "-------------------------------------"<< endl;
 		
     for (FeatureList::iterator i = m_logSpectrum.begin(); i != m_logSpectrum.end(); ++i) {
         Feature f1 = *i;
@@ -264,13 +267,14 @@
         }
 		        
         f2.values.push_back(0.0); f2.values.push_back(0.0); f2.values.push_back(0.0); // upper edge
+
         vector<float> runningmean = SpecialConvolution(f2.values,hw);
         vector<float> runningstd;
-        for (int i = 0; i < 256; i++) { // first step: squared values into vector (variance)
+        for (int i = 0; i < nNote; i++) { // first step: squared values into vector (variance)
             runningstd.push_back((f2.values[i] - runningmean[i]) * (f2.values[i] - runningmean[i]));
         }
         runningstd = SpecialConvolution(runningstd,hw); // second step convolve
-        for (int i = 0; i < 256; i++) { 
+        for (int i = 0; i < nNote; i++) { 
             runningstd[i] = sqrt(runningstd[i]); // square root to finally have running std
             if (runningstd[i] > 0) {
                 // f2.values[i] = (f2.values[i] / runningmean[i]) > thresh ? 
@@ -323,12 +327,12 @@
         f6.hasTimestamp = true;
         f6.timestamp = f2.timestamp;
 	        
-        float b[256];
+        float b[nNote];
 	
         bool some_b_greater_zero = false;
         float sumb = 0;
-        for (int i = 0; i < 256; i++) {
-            // b[i] = m_dict[(256 * count + i) % (256 * 84)];
+        for (int i = 0; i < nNote; i++) {
+            // b[i] = m_dict[(nNote * count + i) % (nNote * 84)];
             b[i] = f2.values[i];
             sumb += b[i];
             if (b[i] > 0) {
@@ -376,12 +380,12 @@
                 float zz[84+1000];
                 int indx[84+1000];
                 int mode;
-                int dictsize = 256*signifIndex.size();
+                int dictsize = nNote*signifIndex.size();
                 // cerr << "dictsize is " << dictsize << "and values size" << f3.values.size()<< endl;
                 float *curr_dict = new float[dictsize];
                 for (unsigned iNote = 0; iNote < signifIndex.size(); ++iNote) {
-                    for (unsigned iBin = 0; iBin < 256; iBin++) {
-                        curr_dict[iNote * 256 + iBin] = 1.0 * m_dict[signifIndex[iNote] * 256 + iBin];
+                    for (unsigned iBin = 0; iBin < nNote; iBin++) {
+                        curr_dict[iNote * nNote + iBin] = 1.0 * m_dict[signifIndex[iNote] * nNote + iBin];
                     }
                 }
                 nnls(curr_dict, nNote, nNote, signifIndex.size(), b, x, &rnorm, w, zz, indx, &mode);
diff -r d398e73b46e0 -r ba930176df5b chromamethods.cpp
--- a/chromamethods.cpp	Wed Nov 10 22:52:46 2010 +0900
+++ b/chromamethods.cpp	Thu Nov 11 10:27:58 2010 +0900
@@ -50,7 +50,7 @@
     int lenConvolvee = convolvee.size();
     int lenKernel = kernel.size();
 
-    vector<float> Z(256,0);
+    vector<float> Z(nNote,0);
     assert(lenKernel % 2 != 0); // no exception handling !!!
     
     for (n = lenKernel - 1; n < lenConvolvee; n++) {
@@ -206,7 +206,7 @@
             // cerr << "curramp" << curr_amp << endl;
             for (unsigned iNote = 0; iNote < nNote; ++iNote) {
                 if (abs(iNote+1.0-floatbin)<2) {
-                    dm[iNote  + 256 * iOut] += cospuls(iNote+1.0, floatbin, binspersemitone + 0.0) * curr_amp;
+                    dm[iNote  + nNote * iOut] += cospuls(iNote+1.0, floatbin, binspersemitone + 0.0) * curr_amp;
                     // dm[iNote + nNote * iOut] += 1 * curr_amp;
                 }
             }
diff -r d398e73b46e0 -r ba930176df5b chromamethods.h
--- a/chromamethods.h	Wed Nov 10 22:52:46 2010 +0900
+++ b/chromamethods.h	Thu Nov 11 10:27:58 2010 +0900
@@ -24,7 +24,7 @@
 
 const int nBPS = 3; // bins per semitone
 const int nOctave = 7;
-const int nNote = nOctave * 12 * nBPS + 2 * (nBPS/2); // a core over all octaves, plus some overlap at top and bottom
+const int nNote = nOctave * 12 * nBPS + 2 * (nBPS/2+1); // a core over all octaves, plus some overlap at top and bottom
 
 extern std::vector<float> SpecialConvolution(std::vector<float> convolvee, std::vector<float> kernel);
 extern void dictionaryMatrix(float* dm, float s_param);